US2026004376A1PendingUtilityA1
Electric vehicle charging and discharging apparatus and method linking ride-sharing service and vehicle-to-grid
Est. expiryJun 28, 2044(~17.9 yrs left)· nominal 20-yr term from priority
G06Q 30/0202H02J 3/003B60L 53/64H02J 2105/12H02J 2105/37H02J 13/14H02J 3/322G06Q 50/43G06Q 50/06G06Q 40/12G06N 3/092G06Q 30/08G06F 17/10B60L 53/00B60L 55/00G06Q 50/40
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Claims
Abstract
An electric vehicle charging and discharging apparatus for linking ride-sharing service and vehicle-to-grid may include a processor and a memory storing software, when executed by the processor, causing the processor to collect power data of buildings, predict power consumption of the buildings based on the power data, respectively, calculate a time zone in which an additional power is required for each of the buildings and a required amount of electrical power based on the collected power data and the predicted power consumption, estimate a travel demand of a region where the buildings exist, and set a travel path of a ride-sharing vehicle for each time zone within the region based on the time zone requiring the additional power, the required amount of electrical power and the travel demand.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . An electric vehicle charging and discharging apparatus for linking ride-sharing service and vehicle-to-grid, comprising:
a processor; and a memory storing software, when executed by the processor, causing the processor to:
collect power data of buildings,
predict power consumption of the buildings based on the power data, respectively,
calculate a time zone in which an additional power is required for each of the buildings and a required amount of electrical power based on the collected power data and the predicted power consumption,
estimate a travel demand of a region where the buildings exist, and
set a travel path of a ride-sharing vehicle for each time zone within the region based on the time zone requiring the additional power, the required amount of electrical power and the travel demand.
2 . The apparatus of claim 1 , wherein the processor is configured to collect location data of respective buildings for estimation of past power usage data, power facility data, contacted power data and the travel demand of respective buildings, for power prediction.
3 . The apparatus of claim 2 , wherein the processor is configured to predict a power usage pattern during a specific period for each of the buildings by using the collected past power usage data.
4 . The apparatus of claim 2 , wherein the processor is configured to estimate the time zone requiring discharging of each of the buildings and the amount of electrical power in consideration of the power facility data and the contacted power data with respect to each of the buildings.
5 . The apparatus of claim 1 , wherein the processor is configured to collect public transportation demand data and ride-sharing service participation record data according to regions, for each time zone, to generate travel demand information.
6 . The apparatus of claim 5 , wherein the processor is configured to estimate an actual travel demand when the ride-sharing vehicle performs movement between building in consideration of the generated travel demand information.
7 . The apparatus of claim 6 , wherein:
the processor is configured to estimate the travel demand through Equation 1 below by using the number of persons to depart from a departure region and the number of persons to move to another region,
X
A
→
B
=
Arrival
quantity
B
∑
n
≠
A
Arrival
quantity
B
×
Departure
quantity
A
[
Equation
1
]
wherein A and B denote buildings, X denotes a travel demand quantity, denotes a travel demand quantity when moving from A to B, n denotes a set of all buildings, an Arrival quantity B is the number of persons moving to a building B, an Arrival quantity n is the number of persons moving to all the buildings, and a Departure quantity A is the number of persons to depart from a building A.
8 . The apparatus of claim 1 , wherein the processor is configured to set a travel path maximizing profitability, and
wherein the profitability is calculated through Equation 2 below,
profitability
=
passenger
boarding
fee
+
electric
vehicle
disharging
fee
+
a
service
fee
of
building
manager
-
electric
vehicle
battery
degradation
cost
-
electric
vehicle
charging
cost
-
electric
vehicle
operating
cost
.
[
Equation
2
]
9 . The apparatus of claim 1 , wherein the processor is configured to use an optimization model comprising reinforcement learning, auction model, and linear programming, in order to set the travel path.
10 . The apparatus of claim 1 , wherein the processor is configured to guide the predetermined path to a customer terminal, and perform reservation with respect to a passenger to use the ride-sharing vehicle according to the path predetermined for each time zone.
11 . An electric vehicle charging and discharging method for linking ride-sharing service and vehicle-to-grid, comprising:
collecting power data of buildings; predicting power consumption of the buildings based on the power data, respectively; calculating a time zone in which an additional power is required for each of the buildings and a required amount of electrical power based on the collected power data and the predicted power consumption; estimating a travel demand of a region where the buildings exist; and setting a travel path of a ride-sharing vehicle for each time zone within the region based on the time zone requiring the additional power, the required amount of electrical power and the travel demand.
12 . The method of claim 11 , wherein the collecting the power data comprises collecting location data of respective buildings for estimation of past power usage data, power facility data, contacted power data and the travel demand of respective buildings, for power prediction.
13 . The method of claim 12 , wherein the predicting the power consumption of the buildings, respectively, comprises predicting a power usage pattern during a specific period for each of the buildings by using the collected past power usage data.
14 . The method of claim 12 , wherein in the calculating the time zone requiring the additional power and the required amount of electrical power, the time zone requiring discharging of each of the buildings and the amount of electrical power are estimated in consideration of the power facility data and the contacted power data with respect to each of the buildings.
15 . The method of claim 11 , wherein the estimating the travel demand comprises generating travel demand information by collecting public transportation demand data and ride-sharing service participation record data according to regions, for each time zone.
16 . The method of claim 15 , wherein the estimating the travel demand further comprises estimating an actual travel demand when the ride-sharing vehicle performs movement between building in consideration of the generated travel demand information.
17 . The method of claim 16 , wherein the estimating the travel demand further comprises estimating the travel demand through Equation 1 below by using the number of persons to depart from a departure region and the number of persons to move to another region,
X
A
→
B
=
Arrival
quantity
B
∑
n
≠
A
Arrival
quantity
B
×
Departure
quantity
A
[
Equation
1
]
wherein A and B denote buildings, X means a travel demand quantity, X A→B denotes a travel demand quantity when moving from A to B, and n denotes a set of all buildings, wherein an Arrival quantity B is the number of persons moving to a building B, an Arrival quantity n is the number of persons moving to all the buildings, and a Departure quantity A is the number of persons to depart from a building A.
18 . The method of claim 11 , wherein the setting the travel path comprises setting a travel path maximizing profitability,
wherein the profitability is calculated through Equation 2 below:
profitability
=
passenger
boarding
fee
+
electric
vehicle
disharging
fee
+
a
service
fee
of
building
manager
-
electric
vehicle
battery
degradation
cost
-
electric
vehicle
charging
cost
-
electric
vehicle
operating
cost
.
[
Equation
2
]
19 . The method of claim 11 , wherein the setting the travel path comprises setting the travel path by using an optimization model comprising reinforcement learning, auction model, and linear programming.
20 . The method of claim 11 , further comprising guiding a predetermined path to a customer terminal, and performing reservation with respect to a passenger to use the ride-sharing vehicle according to the path predetermined for each time zone.Join the waitlist — get patent alerts
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